Mercurial > repos > iuc > genomescope
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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/genomescope commit aa87b7b1713b749328c5a710f32631aab2acaa3a"
author | iuc |
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date | Fri, 30 Apr 2021 20:21:25 +0000 |
parents | |
children | 3169a38c2656 |
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<tool id="genomescope" name="GenomeScope" version="@VERSION@" profile="20.01"> <description>Analyze unassembled short reads</description> <macros> <token name="@VERSION@">2.0</token> </macros> <requirements> <requirement type="package" version="@VERSION@">genomescope2</requirement> </requirements> <version_command>genomescope2 --version</version_command> <command detect_errors="exit_code"><![CDATA[ genomescope2 --input '$input' --output . --kmer_length $kmer_length $no_unique_sequence $testing $trace_flag #if $ploidy: --ploidy $ploidy #end if #if $lambda: --lambda $lambda #end if #if $max_kmercov: --max_kmercov $max_kmercov #end if #if $topology: --topology $topology #end if #if $initial_repetitiveness: --initial_repetitiveness $initial_repetitiveness #end if #if $initial_heterozygosities: --initial_heterozygosities $initial_heterozygosities #end if #if $transform_exp: --transform_exp $transform_exp #end if #if $true_params: --true_params $true_params #end if #if $num_rounds: --num_rounds $num_rounds #end if ]]> </command> <inputs> <param argument="--input" type="data" format="tabular" label="Input histogram file" help="This file is a two column tabular file for example generated with the histo function of Jellyfish."/> <param name="model_output" type="boolean" label="Add the model parameters to your history"/> <param name="summary_output" type="boolean" label="Output a summary of the analysis"/> <param name="progress_output" type="boolean" label="Additional information for each optimization round"/> <param argument="--ploidy" type="integer" optional="true" label="Ploidy for model to use" help="Default: 2"/> <param argument="--kmer_length" type="integer" value="21" optional="false" label="K-mer length used to calculate k-mer spectra"/> <param argument="--lambda" type="integer" optional="true" label="Optional initial kmercov estimate for model to use"/> <param argument="--max_kmercov" type="integer" optional="true" label="Optional maximum k-mer coverage threshold" help="K-mers with coverage greater than max_kmercov are ignored by the model"/> <param argument="--no_unique_sequence" type="boolean" truevalue="--no_unique_sequence" falsevalue="" label="Turn off yellow unique sequence line in plots"/> <param argument="--topology" type="integer" optional="true" label="Flag for topology for model to use"/> <param argument="--initial_repetitiveness" type="integer" optional="true" label="Initial value for repetitiveness"/> <param argument="--initial_heterozygosities" type="integer" optional="true" label="Initial values for nucleotide heterozygosity rates"/> <param argument="--transform_exp" type="integer" optional="true" label="Parameter for the exponent when fitting a transformed (x**transform_exp*y vs. x) k-mer histogram" help="Default: 1"/> <param argument="--testing" type="boolean" truevalue="--testing" falsevalue="" label="Create testing.tsv file with model parameters"/> <param argument="--true_params" type="integer" optional="true" label="Flag to state true simulated parameters for testing mode"/> <param argument="--trace_flag" type="boolean" truevalue="--trace_flag" falsevalue="" label="Turn on printing of iteration progress of nlsLM function"/> <param argument="--num_rounds" type="integer" min="1" optional="true" label="Number of optimization rounds"/> </inputs> <outputs> <data name="linear_plot" format="png" from_work_dir="linear_plot.png" label="${tool.name} on ${on_string} Linear plot"/> <data name="log_plot" format="png" from_work_dir="log_plot.png" label="${tool.name} on ${on_string} Log plot"/> <data name="transformed_linear_plot" format="png" from_work_dir="transformed_linear_plot.png" label="${tool.name} on ${on_string} Transformed linear plot"/> <data name="transformed_log_plot" format="png" from_work_dir="transformed_log_plot.png" label="${tool.name} on ${on_string} Transformed log plot"/> <data name="model" format="txt" from_work_dir="model.txt" label="${tool.name} on ${on_string} Model"> <filter>model_output</filter> </data> <data name="summary" format="txt" from_work_dir="summary.txt" label="${tool.name} on ${on_string} Summary"> <filter>summary_output</filter> </data> <data name="progress" format="txt" from_work_dir="progress.txt" label="${tool.name} on ${on_string} Progress"> <filter>progress_output</filter> </data> <data name="model_params" format="tabular" from_work_dir="SIMULATED_testing.tsv" label="${tool.name} on ${on_string} Model parameters"> <filter>testing</filter> </data> </outputs> <tests> <test expect_num_outputs="8"> <param name="input" value="genomescope-in1.tab"/> <param name="kmer_length" value="21"/> <param name="testing" value="true"/> <param name="model_output" value="true"/> <param name="summary_output" value="true"/> <param name="progress_output" value="true"/> <output name="linear_plot" file="genomescope-out1-1.png" ftype="png"/> <output name="log_plot" file="genomescope-out1-2.png" ftype="png"/> <output name="transformed_linear_plot" file="genomescope-out1-3.png" ftype="png"/> <output name="transformed_log_plot" file="genomescope-out1-4.png" ftype="png" compare="sim_size"/> <output name="model" file="genomescope-out1-1.txt" ftype="txt" lines_diff="2"/> <output name="summary" file="genomescope-out1-2.txt" ftype="txt" lines_diff="2"/> <output name="progress" file="genomescope-out1-3.txt" ftype="txt" lines_diff="2"/> <output name="testing" file="genomescope-out1-1.tab" ftype="tabular"/> </test> </tests> <help><![CDATA[ GenomeScope 2.0: Reference-free profiling of polyploid genomes ============================================================== GenomeScope 2.0 applies classical insights from combinatorial theory to establish a detailed mathematical model of how k-mer frequencies will be distributed in heterozygous and polyploid genomes. GenomeScope 2.0 employs a polyploid-aware mixture model that, within seconds, accurately infers genome properties from unassembled sequencing data. GenomeScope 2.0 uses the k-mer count distribution, e.g. from KMC or Jellyfish, and produces a report and several informative plots describing the genome properties. We validate the approach on simulated polyploid data created using a generative model with parameters for genome size, heterozygosity, repetitiveness, ploidy, and sequencing coverage, and find GenomeScope 2.0 retains accuracy across a broad range of realistic and extreme parameter values. We also validate GenomeScope 2.0 by analyzing genuine sequence data from 11 diverse polyploid genomes with known genome characteristics. ]]></help> <citations> <citation type="doi">10.1093/bioinformatics/btx153</citation> <citation type="doi">10.1038/s41467-020-14998-3</citation> </citations> </tool>